Recommendation area ▸

Data Collection, Analysis, and Reporting

The Problem ▸

Law enforcement in the United States is decentralized, with more than 18,000 law enforcement agencies at the federal, state, and local levels reporting to different governmental structures or democratically elected.[1] Due to this decentralized model, understanding what law enforcement agencies do, how they do it, and how it impacts public safety is challenging. This challenge is exacerbated by data collection, analysis, and reporting processes that vary across jurisdictions and by federal data collection systems with highly variable reporting by agencies and states. This prevents assessments of policing across agencies, at the regional, or national level. The public is left without any reliable way to know what the state of policing is, what is going well, and where there needs improvement. Data collection, analysis, and reporting inconsistencies also hamper policing reform planning and evaluation and limits understanding of racial and other disparities.

What We Know ▸

Council Recommendations ▸

Recommendation 27

Develop a Data Collection, Analysis, and Dissemination Plan

To increase transparency, a comprehensive plan should be developed for collecting, analyzing, and disseminating data, at the incident level, on traffic stops, pedestrian stops, crime incidents, arrests, use of force events, and community complaints. The plan, with input from the community, should include:

  • Performance measures: Agency-wide and unit-specific performance measures that consider input from the community, elected officials, and others.
  • Data collection: A data collection plan that addresses key performance measures and specifies who will collect data, how data should be stored, and what quality assurance measures will be performed.
  • Data sharing: Data sharing plan that includes what data will be shared, with whom, and at what cadence. The plan should also include mechanisms for maintaining the privacy of individuals and adherence to privacy laws.
  • Data analysis: Data should be analyzed periodically to assess performance and identify ineffective or potentially problematic practices and the most effective and promising practices. The results of these analyses, along with supporting data, should be made publicly available in plain language.
Recommendation 28

Maximize the Value of Data by Ensuring Its Consistency

To maximize the understanding of regional, state, and national public safety as well as policing trends, data must be in a consistent format. Funding partners and researchers to make data consistent and unifying and merging data sets to allow comparison with other data sources will help maximize the value of policing data.

Recommendation 29

Design and Implement Data Collection Systems and Processes

Data collection and analysis capacity must be prioritized and funded to achieve the transparency that the public desires.

Recommendation 30

Issue Annual Reports on Activities and Impacts

Every law enforcement agency should issue an annual report and include data on key activities, traffic and pedestrian stops and arrests, and document any impact and outcomes, including disparate impacts to the community. The report should describe actions taken to address disparities and be included with all funding requests.

Recommendation 31

Participate in Federal Data Collection Efforts

Wherever possible, agencies should voluntarily send data to federal data collection systems designed to better inform law enforcement agencies and communities and offer critical, comprehensive, and data-driven evidence to improve the effectiveness and transparency of policing activities. For example, the Federal Bureau of Investigation (FBI) collects voluntarily reported data on officer use of force and the Bureau of Justice Statistics (BJS) periodically collects data on law enforcement agency structure and activity.

Further Research ▸

More research is needed on the best way to report policing administrative data to communities. Administrative data are often complicated and may require sophisticated data management or processing to be useful. Additional research is needed to understand ways to produce and display these data in ways that are accessible to the widest audience.

Additional research is needed to understand how communities best receive and respond to data from law enforcement agencies, including the level of detail desired. Research would also provide insight into how agency transparency is valued in the community and understood, including limitations of the data.

Despite support from agencies, leaders, and stakeholder associations, the FBI’s use-of-force database has fallen short of expectations in terms of adoption and participation. Research would inform the reasons agencies decline to participate and how future efforts should be designed to maximize participation.

Citations ▸

[1] Reaves, B. (2011). Census of State and Local Law Enforcement Agencies, 2008. Washington, DC: U.S. Department of Justice.

[2] See

[3] Office of Community Oriented Policing Services. (2015). Final Report of the President's Task Force on 21st Century Policing. Washington, DC: Office of Community Oriented Policing Services.

[4] Police Data Initiative. (n.d.). Retrieved July 2022, from

[5] For one example of the challenges of collecting police activity data, see Mummolo, J. (2018, August 23), What I Learned by Studying Militarized Policing. The Atlantic.

[6] See for example the Washington Post’s Police Shooting Database ( or the Fatal Encounter dataset (



[9] Buck., S. (2021, April 15). We Need Criminal Justice Data That Doesn’t Exist. Here’s How the Biden Administration Can Fix It. Arnold Ventures.

[10] Zhang, Y., Siriaraya, P., Kawai, Y., & Jatowt, A. (2020). Analysis of street crime predictors in web open data. Journal of Intelligent Information Systems55(3), 535-559.

[11] Almanie, T., Mirza, R., & Lor, E. (2015). Crime prediction based on crime types and using spatial and temporal criminal hotspots. International Journal of Data Mining & Knowledge Management Process, 5(4).

[12] Belesiotis, A., Papadakis, G., & Skoutas, D. (2018). Analyzing and predicting spatial crime distribution using crowdsourced and open data. ACM Transactions on Spatial Algorithms and Systems (TSAS)3(4), 1-31.

[13] Pierson, E., Simoiu, C., Overgoor, J., Corbett-Davies, S., Jenson, D., Shoemaker, A., ... & Goel, S. (2020). A large-scale analysis of racial disparities in police stops across the United States. Nature Human Behaviour4(7), 736-745.

[14] Rivera, R., & Rosenbaum, J. (2020). Racial disparities in police stops in US cities. Significance17(4), 4-5.

[15] Fryer, R. G. (2016). An empirical analysis of racial differences in police use of force (No. w22399). National Bureau of Economic Research.

[16] Khan, M. R., Kapadia, F., Geller, A., Mazumdar, M., Scheidell, J. D., Krause, K. D., ... & Halkitis, P. N. (2021). Racial and ethnic disparities in “stop-and-frisk” experience among young sexual minority men in New York City. PloS One16(8), e0256201.

[17] Zhang, Y., & Zhang, L. (2021). Racial characteristics of areas and police decisions to arrest in traffic stops: multilevel analysis of contextual racial effects. Policing: An International Journal.

[18] Shjarback, J. A., Pyrooz, D. C., Wolfe, S. E., & Decker, S. H. (2017). De-policing and crime in the wake of Ferguson: Racialized changes in the quantity and quality of policing among Missouri police departments. Journal of Criminal Justice50, 42-52.



[21] Stinson, P. M., Liederbach, J., Brewer, S. L., & Todak, N. E. (2014). Drink, drive, go to jail? A study of police officers arrested for drunk driving. Journal of Crime and Justice37(3), 356-376.

[22] Kochel, T. R., & Skogan, W. G. (2021). Accountability and transparency as levers to promote public trust and police legitimacy: findings from a natural experiment. Policing: An International Journal of Police Strategies and Management44(6), 1046-1059.

[23] Mason, D., Hillenbrand, C., & Money, K. (2014). Are informed citizens more trusting? Transparency of performance data and trust towards a British police force. Journal of Business Ethics122(2), 321-341.